Coffee Ratings

Ridgeline plot, Pairwise correlation, Network plot, Singular value decomposition, Linear model

Notable topics: Ridgeline plot, Pairwise correlation, Network plot, Singular value decomposition, Linear model

Recorded on: 2020-07-06

Timestamps by: Eric Fletcher

## Screencast

## Timestamps

Using `fct_lump`

within `count`

and then `mutate`

to lump the variety of coffee together except for the most frequent

Create a `geom_boxplot`

to visualize the variety and the distribution of `total_cup_points`

Create a `geom_histogram`

to visualize the variety and the distribution of `total_cup_points`

Using `fct_reorder`

to reorder `variety`

by sorting it along `total_cup_points`

in ascending order

Using `summarize`

with `across`

to calculate the percent of missing data (NA) for each rating variable

Create a bar chart using `geom_col`

with `fct_lump`

to visualize the frequency of top countries

Using `pivot_longer`

to pivot the rating metrics for wide format to long format

Create a `geom_line`

chart to see if the `sum`

of the rating categories equal to the `total_cup_points`

column

Create a `geom_density_ridges`

chart to show the distribution of ratings across each rating metric

Using `summarize`

with `mean`

and `sd`

to show the average rating per metric with its standard deviation

Using `pairwise_cor`

to find correlations amongst the rating metrics

Create a `network plot`

to show the clustering of the rating metrics

Using `widely_svd`

to visualize the biggest source of variation with the rating metrics (Singular value decomposition)

Create a `geom_histogram`

to visualize the distribution of altitude

Using `pmin`

to set a maximum numeric altitude value of 3000

Create a `geom-point`

chart to visualize the correlation between altitude and quality (`total_cup_points`

)

Using `summarize`

with `cor`

to show the correlation between altitude and each rating metric

Create a linear model `lm`

for each rating metric then visualize the results using a `geom_line`

chart to show how each kilometer of altitude contributes to the score

Summary of screencast